def main(method, transform, temporal, layout, hybrid, threshold, output):
variables = getattr(layouts, layout)
K = list(variables.keys())
df = utils.prep_dataframe(keep=K)
if temporal:
df_shifted = utils.create_shifted_features(df)
df = df.join(df_shifted, how="outer")
df = df.dropna()
if transform:
print("* Tranform data")
X = tr.to_normal(df.values)
df = pd.DataFrame(X, index=df.index.values, columns=df.columns.values)
X = df.values
if hybrid:
model = HRF(k=5, k_star=10, variables_names=df.columns.values)
else:
model = GMRF(method=method[0])
model.fit(X)
if not hybrid:
print("* Selected alpha = {}".format(model.alpha_))
else:
print("* Selected k = {}, k star = {}".format(model.k, model.k_star))
if threshold:
Q = model.precision_.copy()
ts = np.arange(0., 1., 0.001)
bics = np.empty(len(ts))
connectivity = np.empty(len(ts))
n = Q.shape[0]
gmrf_test = GMRF()
gmrf_test.mean_ = np.mean(X, axis=0)
for i, t in enumerate(ts):
Q[Q < t] = 0
gmrf_test.precision_ = Q
bics[i], _ = gmrf_test.bic(X)
connectivity[i] = 1 - np.size(np.where(Q == 0)[0]) / (n * n)
fig, (ax, ax1) = plt.subplots(2, 1)
ax.plot(connectivity)
ax1.plot(bics)
if output:
results_name = os.path.join(os.path.dirname(__file__), "../results/")
if hybrid:
BNs = np.empty(len(model.variables_names), dtype=object)
for i in range(len(BNs)):
BNs[i] = (model.bns[i].variables_names, model.bns[i].nodes, model.bns[i].edges)
np.save(results_name + output + "_bns", BNs)
else:
np.save(results_name + output + "_prec", model.precision_)
np.save(results_name + output + "_mean", model.mean_)
np.save(results_name + output + "_bic_scores", model.bic_scores)
if not hybrid:
plt.figure()
plt.plot(model.bic_scores)
fig, ax = plt.subplots(1, 1)
pl.bin_precision_matrix(model.precision_, df.columns.values, ax)
plt.show()
def main(layout, model, transform, output):
variables = getattr(layouts, layout)
K = list(variables.keys())
df = utils.prep_dataframe(keep=K)
df_shifted = utils.create_shifted_features(df)
df = df.join(df_shifted, how="outer")
df = df.dropna()
# print(list(enumerate(df.columns.values)))
# assert False
if transform:
print("* Tranform data")
X = tr.to_normal(df.values)
df = pd.DataFrame(X, index=df.index.values, columns=df.columns.values)
X = df.values
if model[0] == 'gmrf':
model = GMRF(variables_names=df.columns.values, alpha=0.1)
elif model[0] == 'hybrid':
model = HRF(k=5, k_star=10, variables_names=df.columns.values)
lim = int(X.shape[0] * 0.75)
X_train = X[:lim]
X_test = X[lim:]
model.fit(X_train)
print("* Model Fitted")
# controls_vars = ['ahu_1_outlet', 'ahu_2_outlet', 'ahu_3_outlet', 'ahu_4_outlet']
controls_vars = ['ahu_3_outlet']
controller = Controller(6, 15, 30)
mdp = MDP(model, 1000, reward, 0.8, feature_creator, controller,
controls_vars, n_jobs=3)
mdp.learn()
# plt.figure()
# plt.hist(test[:, 38:42].ravel(), bins=5, range=(5, 30))
# plt.figure()
# plt.plot(test[:, 38:42])
actions, states = run_simulation(X_test, controls_vars, mdp, model, controller)
print(actions)
actions_values_one = [None] * len(controls_vars)
actions_values_two = [None] * len(controls_vars)
for i in range(len(controls_vars)):
actions_values_one[i] = [(j, a[i][1]) for j, a in enumerate(actions)
if a[i][0] == 0]
actions_values_two[i] = [(j, a[i][1]) for j, a in enumerate(actions)
if a[i][0] == 1]
actions_values_one[i] = list(zip(*actions_values_one[i]))
actions_values_two[i] = list(zip(*actions_values_two[i]))
for i in range(len(controls_vars)):
plt.figure()
if len(actions_values_one[i]) != 0:
plt.plot(list(actions_values_one[i][0]), list(actions_values_one[i][1]), 'b')
if len(actions_values_two[i]) != 0:
plt.plot(list(actions_values_two[i][0]), list(actions_values_two[i][1]), 'g')
plt.title(controls_vars[i])
max_states = np.amax(states, axis=1)
mean_states = np.mean(states, axis=1)
min_states = np.amin(states, axis=1)
plt.figure()
plt.plot(max_states, 'r')
plt.plot(mean_states, 'g')
plt.plot(min_states, 'b')
# plt.figure()
# plt.plot(actions[:, 0], label="1")
# plt.plot(actions[:, 1], label="2")
# plt.plot(actions[:, 2], label="3")
# plt.plot(actions[:, 3], label="4")
# plt.legend(loc=0)
# print(np.mean(actions, axis=0))
plt.show()